Quantifying Post- Laser Ablation Prostate Therapy Changes on MRI via a Domain-Specific Biomechanical Model: Preliminary Findings

Prostate Multiparametric Magnetic Resonance Imaging Features Following Partial Gland Cryoablation

OBJECTIVE

To assess the qualitative and quantitative changes on prostate multiparametric magnetic resonance imaging (mpMRI) following partial gland ablation (PGA) with cryotherapy and correlate with histopathology.

METHODS

We used 3D Slicer to generate prostate models and segment ipsilateral (treated) and contralateral peripheral and transition zones in 10 men who underwent MRI/transrectal ultrasound fusion-guided PGA during 2017-2018. Pre- and post-PGA volumes of prostate segments were compared. Post-PGA mpMRI were categorized according to PI-RADS v2 and treatment response on mpMRI was assessed in a manner similar to the radiology evaluation framework following liver lesion ablation.

RESULTS

Median volume of ipsilateral peripheral and transition zones decreased from 10.9 mL and 13.0 mL to 7.2 mL and 10.8 mL (P = .005), respectively. Median volume of contralateral peripheral and transition zones also decreased from 12.1 mL and 12.5 mL to 9.9 mL to 10.4 mL (P = .005), respectively. Five men had clinically significant disease (Grade group ≥2) on post-PGA biopsy (3 within treatment field and 2 outside). Of the men with clinically significant prostate cancer, mpMRI revealed PI-RADS 3 lesions in 2. However, the treatment response framework did not detect residual disease.

CONCLUSION

PGA results in asymmetrical and significant reductions in prostate volume. Our results highlight the need for a separate assessment framework to enable standardization of the interpretation and reporting of post-PGA surveillance mpMRI. Moreover, our findings have significant implications for MRI-targeted surveillance biopsy following PGA with cryotherapy.

3D Registration of mpMRI for Assessment of Prostate Cancer Focal Therapy

RATIONALE AND OBJECTIVES

This study aimed to assess a novel method of three-dimensional (3D) co-registration of prostate magnetic resonance imaging (MRI) examinations performed before and after prostate cancer focal therapy.

MATERIALS AND METHODS

We developed a software platform for automatic 3D deformable co-registration of prostate MRI at different time points and applied this method to 10 patients who underwent focal ablative therapy. MRI examinations were performed preoperatively, as well as 1 week and 6 months post treatment. Rigid registration served as reference for assessing co-registration accuracy and precision.

RESULTS

Segmentation of preoperative and postoperative prostate revealed a significant postoperative volume decrease of the gland that averaged 6.49 cc (P = .017). Applying deformable transformation based on mutual information from 120 pairs of MRI slices, we refined by 2.9 mm (max. 6.25 mm) the alignment of the ablation zone, segmented from contrast-enhanced images on the 1-week postoperative examination, to the 6-month postoperative T2-weighted images. This represented a 500% improvement over the rigid approach (P = .001), corrected by volume. The dissimilarity by Dice index of the mapped ablation zone using deformable transformation vs rigid control was significantly (P = .04) higher at the ablation site than in the whole gland.

CONCLUSIONS

Our findings illustrate our method's ability to correct for deformation at the ablation site. The preliminary analysis suggests that deformable transformation computed from mutual information of preoperative and follow-up MRI is accurate in co-registration of MRI examinations performed before and after focal therapy. The ability to localize the previously ablated tissue in 3D space may improve targeting for image-guided follow-up biopsy within focal therapy protocols.

Theoretical model for laser ablation outcome predictions in brain: calibration and validation on clinical MR thermometry images

PURPOSE

Neurosurgical laser ablation is experiencing a renaissance. Computational tools for ablation planning aim to further improve the intervention. Here, global optimisation and inverse problems are demonstrated to train a model that predicts maximum laser ablation extent.

METHODS

A closed-form steady state model is trained on and then subsequently compared to N = 20 retrospective clinical MR thermometry datasets. Dice similarity coefficient (DSC) is calculated to provide a measure of region overlap between the 57 °C isotherms of the thermometry data and the model-predicted ablation regions; 57 °C is a tissue death surrogate at thermal steady state. A global optimisation scheme samples the dominant model parameter sensitivities, blood perfusion (ω) and optical parameter (μ_eff) values, throughout a parameter space totalling 11 440 value-pairs. This represents a lookup table of μ_eff-ω pairs with the corresponding DSC value for each patient dataset. The μ_eff-ω pair with the maximum DSC calibrates the model parameters, maximising predictive value for each patient. Finally, leave-one-out cross-validation with global optimisation information trains the model on the entire clinical dataset, and compares against the model naïvely using literature values for ω and μ_eff.

RESULTS

When using naïve literature values, the model's mean DSC is 0.67 whereas the calibrated model produces 0.82 during cross-validation, an improvement of 0.15 in overlap with the patient data. The 95% confidence interval of the mean difference is 0.083-0.23 (p < 0.001).

CONCLUSIONS

During cross-validation, the calibrated model is superior to the naïve model as measured by DSC, with +22% mean prediction accuracy. Calibration empowers a relatively simple model to become more predictive.